Abstract 6914: STCS: Spatial transcriptomics cell segmentation outperforms existing methods on multiple slides.

Abstract Spatial transcriptomics (ST) has long been recognized as an advanced technique that provides insights on spatial information beyond what can be obtained from single-cell RNA sequencing. However, widely used sequencing-based ST approaches cannot provide cell level data because their results are aggregated into discrete bins rather than assigned to individual cells. With the advent of Visium HD and other subcellular-resolution platforms, accurate cell segmentation has become essential for extracting biologically meaningful, cell-level information.Here, we present STCS (Spatial Transcriptomics Cell Segmentation), a segmentation framework tailored for high-resolution ST data. We benchmarked STCS against several existing methods—including STHD, bin2cell, and Space Ranger—using a slide with both Visium HD and Xenium results. Evaluation using ground-truth Xenium cell boundary annotations demonstrated that STCS delivers the best performance, achieving 40% accuracy in cell-type prediction and showing the lowest spatial chaos score, a metric that quantifies how spatially continuous clusters are. We also applied STCS to another Visium HD slide from mouse intestinal regeneration model which contains tissue from different time points after radiation. Compared to default Visium HD binning, STCSsegmented cells show clear transcriptional differences by timepoints and identify several rare immune cell types. As a result, downstream analyses such as spatial cell-cell interaction inference and regional pattern characterization can be done in cell level which include more cell types and more immune related pathways like JAK-STAT pathway with STCS. In addition, STCS is versatile and can be applied to other sequencing-based ST methods like Stereo-seq, which offers nanometer-scale resolution. And it’s also an open-source tool with adjustable parameters for different tissue types.In summary, STCS is a robust and flexible cell segmentation tool that provides a one-stop solution for deriving biologically meaningful, cell-level information from high-resolution sequencing-based ST datasets. Citation Format: Xinyu Hu, Fengwei Zhan, Lixia C. Wu, Jose Gonzalez, Chuhanwen Sun, Rachel Ofer, Tyler Tran, Michael Verzi, Jiekun Yang. STCS: Spatial transcriptomics cell segmentation outperforms existing methods on multiple slides abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6914.